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This is a PyTorch reinforcement learning library designed for training agents in simulation environments. It provides a collection of deep reinforcement learning algorithms focusing on policy gradient methods and trust-region optimization.
The main features of ikostrikov/pytorch-a2c-ppo-acktr-gail are: Reinforcement Learning Training, Actor-Critic Architectures, Kronecker-Factored Trust Region Methods, Policy Gradient Methods, Proximal Policy Optimization, PyTorch Reinforcement Learning Libraries, Trust Region Policy Optimization, Simulation Training Environments.
Projects with overlapping indexed features include: openai/baselines — Baselines is a comprehensive suite of frameworks for reinforcement learning algorithm implementation, imitation… p-christ/deep-reinforcement-learning-algorithms-with-pytorch — This is a PyTorch-based toolkit for training reinforcement learning agents, providing implementations of standard and… andri27-ts/reinforcement-learning — This project is a collection of reinforcement learning implementations and educational materials written in Python. It… facebookresearch/horizon — Horizon is a reinforcement learning platform designed for training, evaluating, and deploying agents and contextual… lazyprogrammer/machine_learning_examples — This project is a comprehensive collection of practical code examples and implementation libraries for machine… morvanzhou/tutorials — This repository is a comprehensive collection of instructional guides and practical examples for Python development,…
Baselines is a comprehensive suite of frameworks for reinforcement learning algorithm implementation, imitation learning, and training orchestration. It provides a library of standardized learning algorithms used to benchmark and replicate research results, alongside a deep learning policy framework for constructing neural network architectures such as multi-layer perceptrons, convolutional networks, and long short-term memory networks. The project includes a specialized imitation learning toolkit that enables agents to mimic expert behavior through behavior cloning and generative adversarial
This is a PyTorch-based toolkit for training reinforcement learning agents, providing implementations of standard and hierarchical deep RL algorithms. It is designed as a library for deep reinforcement learning research and experimentation, supporting both discrete and continuous control tasks through a collection of algorithm implementations. The project distinguishes itself by offering a hierarchical reinforcement learning framework that decomposes complex long-horizon tasks into manageable sub-goals using meta-controllers and lower-level policies. It also includes a Hindsight Experience Re
This project is a collection of reinforcement learning implementations and educational materials written in Python. It provides neural network architectures for solving control tasks through deep reinforcement learning, spanning value-based and policy-gradient methods. The repository includes a library of evolutionary strategies and genetic algorithms as alternatives to gradient-based learning. It also features a model-based system for predicting future environment states and rewards to enable internal simulation and offline planning. The codebase covers a wide range of capabilities, includi
Horizon is a reinforcement learning platform designed for training, evaluating, and deploying agents and contextual bandits using historical data. It serves as an off-policy engine and offline policy evaluation tool, allowing decision-making policies to be optimized and tested without the need for a live simulator. The framework specializes in recommendation system optimization, specifically using slating-based reinforcement learning to optimize the ordering and sequencing of multiple recommendations. It also functions as a contextual bandit framework that manages the balance between explorat